Data stream processing in dynamic and decentralized peer-to-peer networks

Data stream management systems (DSMS) process data streams, potentially infinite amounts of data sent by active data sources. Distributed DSMS use networks of interconnected machines to enhance the processing power. Typically, clusters of equal, non-autonomous machines are used. However, in some applications, a cluster of computers is not available, not feasible, their acquisition costs are too high or they are too complex to deploy. An alternative would be to use a collection of notebooks, personal computers or smartphones, resulting in a network which only contains autonomous and heterogeneous machines. This results in a dynamic and decentralized network which has to be considered in distributed data stream processing. In this paper, I present my PhD project for developing and deploying a distributed DSMS that can be executed in a Peer-to-Peer (P2P) network of autonomous and heterogeneous peers. My approach addresses three main challenges: data source management, continuous query distribution and distributed query management. A prototypical implementation is already in place and the evaluation is currently planned.